{"id":"W1980538968","doi":"10.1002/cplx.20100","title":"Modeling pathways of differentiation in genetic regulatory networks with Boolean networks","year":2005,"lang":"en","type":"article","venue":"Complexity","topic":"Gene Regulatory Network Analysis","field":"Biochemistry, Genetics and Molecular Biology","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"National Institutes of Health; National Science Foundation","keywords":"Attractor; Observable; Cellular differentiation; Nonlinear system; Perturbation (astronomy); Computer science; Gene regulatory network; Variety (cybernetics); Gene; Topology (electrical circuits); Biology; Biological system; Mathematics; Physics; Genetics; Gene expression; Artificial intelligence; Mathematical analysis","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008059695,0.0005189197,0.0004361348,0.0006285438,0.00031349,0.0008810234,0.0007055347,0.0007619809,0.001267725],"category_scores_gemma":[0.002986835,0.0003521259,0.0006780574,0.0005017663,0.0008178408,0.001416776,0.0004675506,0.0007212812,0.0001409898],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001540963,"about_ca_system_score_gemma":0.0005794877,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004693103,"about_ca_topic_score_gemma":0.004883127,"domain_scores_codex":[0.9996911,0.000142723,0.00001316025,0.00004832438,0.00005845382,0.00004629448],"domain_scores_gemma":[0.998854,0.0008256194,0.0001684787,0.00004887217,0.00005446291,0.00004858985],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003802502,0.00001759169,0.0003935057,0.00001726227,0.000009428033,0.00002437599,0.0000307032,0.9684572,0.002589432,0.02709429,0.00004913057,0.001279048],"study_design_scores_gemma":[0.000007194561,0.000007008382,0.00005020202,0.000001305586,0.000003146079,0.000003600797,0.000002922618,0.9928577,0.0003441417,0.006620003,0.0001007864,0.000001986801],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4827049,0.0004097465,0.5099418,0.0005825933,0.0000297416,0.00007565751,0.0003256558,0.0002816332,0.00564825],"genre_scores_gemma":[0.9448514,0.0004468312,0.05227106,0.00006323867,0.0000224394,0.0001536467,0.0001749694,0.00002880826,0.001987726],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004693103,"threshold_uncertainty_score":0.01118052,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02102476204328716,"score_gpt":0.2190382291684405,"score_spread":0.1980134671251533,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}